class_docstr: Adapt pandas transformations to sktime interface.
  
  In `transform`, executes `pd.DataFrame` method of name `method` on data,
  optionally with keywords arguments passed, via `kwargs` hyper-parameter.
  The `apply_to` parameter controls what the data is upon which `method` is called:
  "call" = for `X` seen in `transform`, "all"/"all_subset" = all data seen so far.
  See below for details.
  
  For hierarchical series, operation is applied by instance.
  
  Parameters
  ----------
  method : str, optional, default = None = identity transform
      name of the method of DataFrame that is applied in transform
  kwargs : dict, optional, default = empty dict (no kwargs passed to method)
      arguments passed to DataFrame.method
  apply_to : str, one of "call", "all", "all_subset", optional, default = "call"
      "call" = method is applied to `X` seen in transform only
      "all" = method is applied to all `X` seen in `fit`, `update`, `transform`
          more precisely, the application to `self._X` is returned
      "all_subset" = method is applied to all `X` like for "all" value,
          but before returning, result is sub-set to indices of `X` in `transform`
      in "all", "all_subset", `X` seen in `transform` do not update `self._X`
  
  Examples
  --------
  >>> from sktime.transformations.series.adapt import PandasTransformAdaptor
  >>> from sktime.datasets import load_airline
  >>> y = load_airline()
  
  >>> transformer = PandasTransformAdaptor("diff")
  >>> y_hat = transformer.fit_transform(y)
  
  >>> transformer = PandasTransformAdaptor("diff", apply_to="all_subset")
  >>> y_hat = transformer.fit(y.iloc[:12])
  >>> y_hat = transformer.transform(y.iloc[12:])
class_docstr_tok: ["Adapt","pandas","transformations","to","sktime","interface",".","In","`","transform","`",",","executes","`","pd.DataFrame","`","method","of","name","`","method","`","on","data",",","optionally","with","keywords","arguments","passed",",","via","`","kwargs","`","hyper-parameter",".","The","`","apply_to","`","parameter","controls","what","the","data","is","upon","which","`","method","`","is","called",":","''","call","''","=","for","`","X","`","seen","in","`","transform","`",",","``","all","''","\/","''","all_subset","''","=","all","data","seen","so","far",".","See","below","for","details",".","For","hierarchical","series",",","operation","is","applied","by","instance",".","Parameters","--","--","--","--","--","method",":","str",",","optional",",","default","=","None","=","identity","transform","name","of","the","method","of","DataFrame","that","is","applied","in","transform","kwargs",":","dict",",","optional",",","default","=","empty","dict","(","no","kwargs","passed","to","method",")","arguments","passed","to","DataFrame.method","apply_to",":","str",",","one","of","``","call","''",",","``","all","''",",","``","all_subset","''",",","optional",",","default","=","``","call","''","``","call","''","=","method","is","applied","to","`","X","`","seen","in","transform","only","``","all","''","=","method","is","applied","to","all","`","X","`","seen","in","`","fit","`",",","`","update","`",",","`","transform","`","more","precisely",",","the","application","to","`","self._X","`","is","returned","``","all_subset","''","=","method","is","applied","to","all","`","X","`","like","for","``","all","''","value",",","but","before","returning",",","result","is","sub-set","to","indices","of","`","X","`","in","`","transform","`","in","``","all","''",",","``","all_subset","''",",","`","X","`","seen","in","`","transform","`","do","not","update","`","self._X","`","Examples","--","--","--","--",">",">",">","from","sktime.transformations.series.adapt","import","PandasTransformAdaptor",">",">",">","from","sktime.datasets","import","load_airline",">",">",">","y","=","load_airline","(",")",">",">",">","transformer","=","PandasTransformAdaptor","(","``","diff","''",")",">",">",">","y_hat","=","transformer.fit_transform","(","y",")",">",">",">","transformer","=","PandasTransformAdaptor","(","``","diff","''",",","apply_to=","''","all_subset","''",")",">",">",">","y_hat","=","transformer.fit","(","y.iloc","[",":12","]",")",">",">",">","y_hat","=","transformer.transform","(","y.iloc","[","12",":","]",")"]
docstr: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str, default="default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
  
  Returns
  -------
  params : dict or list of dict, default = {}
      Parameters to create testing instances of the class
      Each dict are parameters to construct an "interesting" test instance, i.e.,
      `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
      `create_test_instance` uses the first (or only) dictionary in `params`
docstr_tok: ["Return","testing","parameter","settings","for","the","estimator",".","Parameters","--","--","--","--","--","parameter_set",":","str",",","default=","''","default","''","Name","of","the","set","of","test","parameters","to","return",",","for","use","in","tests",".","If","no","special","parameters","are","defined","for","a","value",",","will","return","`","``","default","''","`","set",".","Returns","--","--","--","-","params",":","dict","or","list","of","dict",",","default","=","{","}","Parameters","to","create","testing","instances","of","the","class","Each","dict","are","parameters","to","construct","an","``","interesting","''","test","instance",",","i.e.",",","`","MyClass","(","*","*","params",")","`","or","`","MyClass","(","*","*","params","[","i","]",")","`","creates","a","valid","test","instance",".","`","create_test_instance","`","uses","the","first","(","or","only",")","dictionary","in","`","params","`"]
code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str, default="default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
  
      Returns
      -------
      params : dict or list of dict, default = {}
          Parameters to create testing instances of the class
          Each dict are parameters to construct an "interesting" test instance, i.e.,
          `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
          `create_test_instance` uses the first (or only) dictionary in `params`
      """
      params1 = {"method": "diff"}
      params2 = {
          "method": "diff",
          "kwargs": {"periods": 2},
          "apply_to": "all_subset",
      }
      params3 = {
          "method": "shift",
          "kwargs": {"periods": 12},
          "apply_to": "all",
      }
  
      return [params1, params2, params3]
  
file_path: sktime/sktime/transformations/series/adapt.py
imports: from inspect import signature
  import numpy
  from sklearn.base import clone
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 3 for calling the PandasTransformAdaptor obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list